V
Vincent Claveau
Researcher at Institut de Recherche en Informatique et Systèmes Aléatoires
Publications - 105
Citations - 931
Vincent Claveau is an academic researcher from Institut de Recherche en Informatique et Systèmes Aléatoires. The author has contributed to research in topics: Language model & Inductive logic programming. The author has an hindex of 15, co-authored 100 publications receiving 838 citations. Previous affiliations of Vincent Claveau include French Institute for Research in Computer Science and Automation & Centre national de la recherche scientifique.
Papers
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Proceedings ArticleDOI
Language modeling for bag-of-visual words image categorization
TL;DR: Two ways of improving image classification based on bag-of-words representation are proposed and new techniques to eliminate useless words are proposed, one based on geometric properties of the keypoints, the other on the use of probabilistic Latent Semantic Analysis (pLSA).
Book ChapterDOI
Automatic morphological query expansion using analogy-based machine learning
TL;DR: A simple yet effective method to recognize morphological variants of information retrieval systems that are further used so as to enrich queries and which does not need any external resources or a priori knowledge and thus supports many languages.
Journal ArticleDOI
Automated Classification of Free-text Pathology Reports for Registration of Incident Cases of Cancer
Vianney Jouhet,Gautier Defossez,Anita Burgun,P. Le Beux,Pierre Levillain,Pierre Ingrand,Vincent Claveau +6 more
TL;DR: The results suggest that free-text pathology reports could be useful as a data source for automated systems in order to identify and notify new cases of cancer.
Proceedings ArticleDOI
CAS: French Corpus with Clinical Cases
TL;DR: The CAS corpus built with clinical cases, such as they are reported in the published scientific literature in French, is proposed, currently containing over 397,000 word occurrences, and the existing linguistic and semantic annotations are described.
Proceedings ArticleDOI
Distances and weighting schemes for bag of visual words image retrieval
TL;DR: This study proposes the use of term weighting techniques and classical distances from text retrieval in the case of images, and provides some interesting insights about the semantic and statistical differences between textual and visual words, and about the way visual word-based image retrieval systems can be optimized.